Sophisticated biological systems, as often occur in nature, such as cells, tissues, and organs in the human body for example, are capable of responding to external chemical and/or physical stimuli (“input variables” in some systems parlance). Such stimuli can additionally be applied as spatial or temporal gradients. The responses of biological systems can nonlinearly depend on the stimuli in a complex interplay of multiple variables comprising external stimuli from the environment and internal factors within the biological system. This interplay can involve synergistic and antagonistic relationship among the multiple input variables.
As a result of such complexity, It can be difficult to manipulate a biological system, such as a cell, a group of cells, and organ or tissue, to behave in a desirable or nearly optimal way without understanding the following: (i) the effects that each input variable (e.g., type of control input) has on a system, (ii) the different possible states of each variable (specific parameter of the control input), (iii) how those states affect the overall system, and (iv) the effects of interactions among the variables. The inability to manipulate a complex biological system posts challenges to elucidating mechanisms of cellular processes although such information can contribute significantly to the advancement of basic research and medical applications. Further, cellular processes can be dynamic, stochastic, nonlinear, multi-parametric, and/or possess memory effects. An example is cells which regulate their activities by integrating multiple external stimuli using internal and external cellular signal transduction networks. A signal transduction network is a cascade of biochemical reactions in the cells that can modify the cellular activity, such as a transcription factor activity in response to the binding of an external stimulus such as a ligand that binds to a corresponding receptor.
In a biological experiment for understanding the above-described signal transduction pathway, each stimulus, for example a ligand such as a drug or a cytokine, or other changes in the physical or chemical environment, can be tested independently, via an independent variation approach. Elements in signal transduction pathways that responds to a stimulus can be identified in an effort to reconstruct the signal transduction pathway and associated biological responses from downstream cellular processes. Due to the complexity of biological systems, the transduction pathway identification and reconstruction process can be extremely time-consuming and typically provides only partial information on the pathway. Furthermore, interactions between or among stimuli can be missed by such an independent variation approach. Therefore, important information in the combinatorial control, which can occur naturally in biological systems, is unavailable.
In combinatorial control, the combinations of the inputs can interact nonlinearly, in an antagonistic and/or a synergistic fashion. Instead of one to one correspondence of the input and output relationships, specific combinations can result in different responses. For example, it is estimated that only about 160 transcription factors exist in the yeast genome, but yeast cells contain thousands of co-regulated sets of genes. Further, it is difficult to determine combinations of control inputs, such as a specific ligands and specific concentrations of the ligands, for eliciting a desired response from a biological sample representing a biological system. Examples of other biological experiments that illustrate problems associated with the determination of combinatorial control include specific combinations of drugs and their corresponding concentrations in combination drug treatment, or in identifying the proper combination of environment cues in nature for a specific biological response.
One approach is to test all the combinations of the different stimuli (e.g., different ligands, or chemical or physical conditions) and the different states of each stimulus (e.g., different concentrations of a specific ligand, or different values of pH, temperature, shear stress, electrical field, magnetic field, etc.). However, the number of tests (experiments) required increases exponentially with the number of different stimuli (or “input variables” or “control inputs” in some systems terminology). The number of tests can become impracticably large in terms of cost and time for large numbers of input variables. For example, testing the effectiveness of a six-drug combination cocktail on a tissue or cell sample, assuming: only ten different concentrations per drug is used, requires 106 or one million tests in order to identify a nearly optimal blend of concentrations. The identification of substantially optimal stimulus conditions for a desired biological response with a more limited number of tests is very desirable.
For decades, flu vaccines have been manufactured growing viruses in millions of live, fertilized eggs. The system works well, but it is time consuming and hard to ramp up quickly in a public health emergency. The threat of a flu pandemic demands new methods for developing newer, faster production systems for vaccines, including vaccines for flu, SARS and the like. Cell-based production methods grow the flu virus in steel vats filled with living cells derived from monkeys, dogs, humans or even insects. Some vaccines produced this way have won limited approval in Europe, but none has been cleared for use in the United States.